{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mrSaggio/mrSaggio/blob/main/yolov5_tutorial_train_with_custom_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "49d4263b",
      "metadata": {
        "papermill": {
          "duration": 0.008151,
          "end_time": "2022-06-29T17:55:09.971303",
          "exception": false,
          "start_time": "2022-06-29T17:55:09.963152",
          "status": "completed"
        },
        "tags": [],
        "id": "49d4263b"
      },
      "source": [
        "# About"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "58f36279",
      "metadata": {
        "papermill": {
          "duration": 0.007274,
          "end_time": "2022-06-29T17:55:09.986301",
          "exception": false,
          "start_time": "2022-06-29T17:55:09.979027",
          "status": "completed"
        },
        "tags": [],
        "id": "58f36279"
      },
      "source": [
        "This notebook show how to train YOLOv5 object detector using custom data. For this purpose I created [Dataset](https://www.kaggle.com/datasets/maxkav/yolov5-game-dataset-for-darknet-framework?sort=recent-comments&select=notes.json) of images and labels.\n",
        "\n",
        "Steps of all the process:\n",
        "1. Collect lots of images.\n",
        "2. Label images using labeling tool.\n",
        "3. Train model and get weights file.\n",
        "4. Initialize model with weights file & use it."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "917f0e16",
      "metadata": {
        "papermill": {
          "duration": 0.006901,
          "end_time": "2022-06-29T17:55:10.000517",
          "exception": false,
          "start_time": "2022-06-29T17:55:09.993616",
          "status": "completed"
        },
        "tags": [],
        "id": "917f0e16"
      },
      "source": [
        "# Initialize constants"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "430f3558",
      "metadata": {
        "papermill": {
          "duration": 0.007286,
          "end_time": "2022-06-29T17:55:10.014754",
          "exception": false,
          "start_time": "2022-06-29T17:55:10.007468",
          "status": "completed"
        },
        "tags": [],
        "id": "430f3558"
      },
      "source": [
        "Set BASE_MODEL according to [Pretrained Checkpoints](https://github.com/ultralytics/yolov5/releases)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "00f6c6be",
      "metadata": {
        "papermill": {
          "duration": 0.022968,
          "end_time": "2022-06-29T17:55:10.045179",
          "exception": false,
          "start_time": "2022-06-29T17:55:10.022211",
          "status": "completed"
        },
        "tags": [],
        "id": "00f6c6be"
      },
      "outputs": [],
      "source": [
        "PROJECT_NAME = \"yolov5_train\"\n",
        "BASE_MODEL = \"yolov5m6.pt\"\n",
        "TRAIN_BATCH = 32\n",
        "TRAIN_EPOCHS = 200\n",
        "VAL_BATCH = 64"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yj6e9NcqvH1g",
        "outputId": "03200a55-0823-4f15-faf9-6cef2fefc0a8"
      },
      "id": "yj6e9NcqvH1g",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e7d4456a",
      "metadata": {
        "papermill": {
          "duration": 0.007061,
          "end_time": "2022-06-29T17:55:10.059449",
          "exception": false,
          "start_time": "2022-06-29T17:55:10.052388",
          "status": "completed"
        },
        "tags": [],
        "id": "e7d4456a"
      },
      "source": [
        "# Clone yolov5 repo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "40b19497",
      "metadata": {
        "papermill": {
          "duration": 3.336127,
          "end_time": "2022-06-29T17:55:13.402600",
          "exception": false,
          "start_time": "2022-06-29T17:55:10.066473",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "40b19497",
        "outputId": "237a21da-d400-44bc-e34c-3819f5a3ec80"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'yolov5'...\n",
            "remote: Enumerating objects: 16512, done.\u001b[K\n",
            "remote: Counting objects: 100% (104/104), done.\u001b[K\n",
            "remote: Compressing objects: 100% (89/89), done.\u001b[K\n",
            "remote: Total 16512 (delta 41), reused 49 (delta 15), pack-reused 16408\u001b[K\n",
            "Receiving objects: 100% (16512/16512), 15.12 MiB | 22.50 MiB/s, done.\n",
            "Resolving deltas: 100% (11306/11306), done.\n"
          ]
        }
      ],
      "source": [
        "! git clone https://github.com/ultralytics/yolov5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "8552a334",
      "metadata": {
        "papermill": {
          "duration": 11.792352,
          "end_time": "2022-06-29T17:55:25.204542",
          "exception": false,
          "start_time": "2022-06-29T17:55:13.412190",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8552a334",
        "outputId": "b6ac25f0-5bd3-4bcf-845e-76e0d8680b84"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m195.4/195.4 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m719.5/719.5 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "! pip install -qr yolov5/requirements.txt"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "052e2f95",
      "metadata": {
        "papermill": {
          "duration": 0.008698,
          "end_time": "2022-06-29T17:55:25.222695",
          "exception": false,
          "start_time": "2022-06-29T17:55:25.213997",
          "status": "completed"
        },
        "tags": [],
        "id": "052e2f95"
      },
      "source": [
        "# Import libraries"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "47596d90",
      "metadata": {
        "papermill": {
          "duration": 0.00904,
          "end_time": "2022-06-29T17:55:25.240623",
          "exception": false,
          "start_time": "2022-06-29T17:55:25.231583",
          "status": "completed"
        },
        "tags": [],
        "id": "47596d90"
      },
      "source": [
        "This notebook contains steps to train and evaluate yolov5 model with custom data from scratch.\n",
        "\n",
        "Steps to reproduce:\n",
        "1. Collect lots of images.\n",
        "2. Label images using labeling tool.\n",
        "4. Train model and get weights file.\n",
        "5. Initialize model with weights file & use it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "7f6bf711",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-29T17:55:25.259842Z",
          "iopub.status.busy": "2022-06-29T17:55:25.259540Z",
          "iopub.status.idle": "2022-06-29T17:55:27.729838Z",
          "shell.execute_reply": "2022-06-29T17:55:27.728727Z"
        },
        "papermill": {
          "duration": 2.483532,
          "end_time": "2022-06-29T17:55:27.733029",
          "exception": false,
          "start_time": "2022-06-29T17:55:25.249497",
          "status": "completed"
        },
        "tags": [],
        "id": "7f6bf711"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from yolov5 import utils\n",
        "import torch\n",
        "from IPython import display\n",
        "from IPython.display import clear_output\n",
        "from pathlib import Path\n",
        "import yaml\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "import glob\n",
        "import io\n",
        "import os\n",
        "import cv2\n",
        "import json\n",
        "import shutil\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "280f3918",
      "metadata": {
        "papermill": {
          "duration": 0.008731,
          "end_time": "2022-06-29T17:55:27.751136",
          "exception": false,
          "start_time": "2022-06-29T17:55:27.742405",
          "status": "completed"
        },
        "tags": [],
        "id": "280f3918"
      },
      "source": [
        "# Convert data to yolov5 Pytorch format\n",
        "\n",
        "Prepare data from Label Studio yolov5 darknet format to pytorch yolov5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "424384b9",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-29T17:55:27.770259Z",
          "iopub.status.busy": "2022-06-29T17:55:27.769749Z",
          "iopub.status.idle": "2022-06-29T17:55:27.774292Z",
          "shell.execute_reply": "2022-06-29T17:55:27.773380Z"
        },
        "papermill": {
          "duration": 0.016207,
          "end_time": "2022-06-29T17:55:27.776130",
          "exception": false,
          "start_time": "2022-06-29T17:55:27.759923",
          "status": "completed"
        },
        "tags": [],
        "id": "424384b9"
      },
      "outputs": [],
      "source": [
        "IMAGES_PATH = \"/content/drive/MyDrive/truba/images\"\n",
        "LABELS_PATH = \"/content/drive/MyDrive/truba/labels\"\n",
        "NOTES_PATH = \"/content/drive/MyDrive/truba/notes.json\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "f5828884",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-29T17:55:27.795693Z",
          "iopub.status.busy": "2022-06-29T17:55:27.794919Z",
          "iopub.status.idle": "2022-06-29T17:55:27.881289Z",
          "shell.execute_reply": "2022-06-29T17:55:27.879827Z"
        },
        "papermill": {
          "duration": 0.098256,
          "end_time": "2022-06-29T17:55:27.883285",
          "exception": false,
          "start_time": "2022-06-29T17:55:27.785029",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f5828884",
        "outputId": "868615e1-f656-4ead-e1e0-91d66ecf55f6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train: 12; valid: 2; test: 1\n"
          ]
        }
      ],
      "source": [
        "# Read labels\n",
        "labels = os.listdir(LABELS_PATH)\n",
        "\n",
        "# Split data\n",
        "train, test = train_test_split(labels, test_size=0.15, shuffle=True)\n",
        "valid, test = train_test_split(test, test_size=0.2)\n",
        "\n",
        "print(f\"train: {len(train)}; valid: {len(valid)}; test: {len(test)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ba466b99",
      "metadata": {
        "papermill": {
          "duration": 0.008665,
          "end_time": "2022-06-29T17:55:27.900890",
          "exception": false,
          "start_time": "2022-06-29T17:55:27.892225",
          "status": "completed"
        },
        "tags": [],
        "id": "ba466b99"
      },
      "source": [
        "Make dirs for pytorch dataset format"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "44cfc291",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-29T17:55:27.920425Z",
          "iopub.status.busy": "2022-06-29T17:55:27.919620Z",
          "iopub.status.idle": "2022-06-29T17:55:27.925176Z",
          "shell.execute_reply": "2022-06-29T17:55:27.924356Z"
        },
        "papermill": {
          "duration": 0.01729,
          "end_time": "2022-06-29T17:55:27.927113",
          "exception": false,
          "start_time": "2022-06-29T17:55:27.909823",
          "status": "completed"
        },
        "tags": [],
        "id": "44cfc291"
      },
      "outputs": [],
      "source": [
        "os.makedirs(\"test/images\")\n",
        "os.makedirs(\"test/labels\")\n",
        "os.makedirs(\"train/images\")\n",
        "os.makedirs(\"train/labels\")\n",
        "os.makedirs(\"valid/images\")\n",
        "os.makedirs(\"valid/labels\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "d8e9e005",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-29T17:55:27.946835Z",
          "iopub.status.busy": "2022-06-29T17:55:27.945982Z",
          "iopub.status.idle": "2022-06-29T17:55:36.390527Z",
          "shell.execute_reply": "2022-06-29T17:55:36.389330Z"
        },
        "papermill": {
          "duration": 8.457287,
          "end_time": "2022-06-29T17:55:36.393234",
          "exception": false,
          "start_time": "2022-06-29T17:55:27.935947",
          "status": "completed"
        },
        "tags": [],
        "id": "d8e9e005"
      },
      "outputs": [],
      "source": [
        "def move_files_to_dir(files, dirname):\n",
        "    for label_filename in files:\n",
        "        image_filename = f\"{label_filename[:-4]}.jpg\"\n",
        "        shutil.copy(f\"{IMAGES_PATH}/{image_filename}\", f\"{dirname}/images/{image_filename}\")\n",
        "        shutil.copy(f\"{LABELS_PATH}/{label_filename}\", f\"{dirname}/labels/{label_filename}\")\n",
        "\n",
        "# Move splits to folders\n",
        "move_files_to_dir(train, \"train\")\n",
        "move_files_to_dir(test, \"test\")\n",
        "move_files_to_dir(valid, \"valid\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b73bc999",
      "metadata": {
        "papermill": {
          "duration": 0.009168,
          "end_time": "2022-06-29T17:55:36.411892",
          "exception": false,
          "start_time": "2022-06-29T17:55:36.402724",
          "status": "completed"
        },
        "tags": [],
        "id": "b73bc999"
      },
      "source": [
        "Convert yolov5-darknet to yolov5-pytorch description file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "c46e5bd5",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-29T17:55:36.431675Z",
          "iopub.status.busy": "2022-06-29T17:55:36.431350Z",
          "iopub.status.idle": "2022-06-29T17:55:36.440499Z",
          "shell.execute_reply": "2022-06-29T17:55:36.438977Z"
        },
        "papermill": {
          "duration": 0.02236,
          "end_time": "2022-06-29T17:55:36.443337",
          "exception": false,
          "start_time": "2022-06-29T17:55:36.420977",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c46e5bd5",
        "outputId": "2742e8f6-6653-4c62-b72d-33847350b122"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train: ../train/images\n",
            "test: ../test/images\n",
            "val: ../valid/images\n",
            "\n",
            "nc: 20\n",
            "names: ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'А', 'В', 'Г', 'З', 'К', 'О', 'П', 'С', 'Т', 'У']\n"
          ]
        }
      ],
      "source": [
        "descr_darknet = json.load(open(NOTES_PATH))\n",
        "\n",
        "train_path = \"../train/images\"\n",
        "test_path = \"../test/images\"\n",
        "valid_path = \"../valid/images\"\n",
        "\n",
        "nc = len(descr_darknet[\"categories\"])\n",
        "names = [category[\"name\"] for category in descr_darknet[\"categories\"]]\n",
        "\n",
        "print(\n",
        "    f\"train: {train_path}\\n\"\n",
        "    f\"test: {test_path}\\n\"\n",
        "    f\"val: {valid_path}\\n\\n\"\n",
        "    f\"nc: {nc}\\n\"\n",
        "    f\"names: {names}\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "02930686",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-29T17:55:36.463918Z",
          "iopub.status.busy": "2022-06-29T17:55:36.463610Z",
          "iopub.status.idle": "2022-06-29T17:55:36.471147Z",
          "shell.execute_reply": "2022-06-29T17:55:36.470296Z"
        },
        "papermill": {
          "duration": 0.019927,
          "end_time": "2022-06-29T17:55:36.473117",
          "exception": false,
          "start_time": "2022-06-29T17:55:36.453190",
          "status": "completed"
        },
        "tags": [],
        "id": "02930686"
      },
      "outputs": [],
      "source": [
        "with open(\"data.yaml\", \"w\") as file:\n",
        "    yaml.dump({\n",
        "        \"train\": train_path,\n",
        "        \"test\": test_path,\n",
        "        \"val\": valid_path,\n",
        "        \"nc\": nc,\n",
        "        \"names\": [f'{name}' for name in names]\n",
        "    }, stream=file, default_flow_style=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "a2f330ff",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-29T17:55:36.492412Z",
          "iopub.status.busy": "2022-06-29T17:55:36.491648Z",
          "iopub.status.idle": "2022-06-29T17:55:37.182766Z",
          "shell.execute_reply": "2022-06-29T17:55:37.181678Z"
        },
        "papermill": {
          "duration": 0.703033,
          "end_time": "2022-06-29T17:55:37.185066",
          "exception": false,
          "start_time": "2022-06-29T17:55:36.482033",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2f330ff",
        "outputId": "0d773164-5981-47bb-8ca2-f98b989287e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Now we are ready to train yolov5 model\n",
            "data.yaml  drive  sample_data  test  train  valid  yolov5  yolov5m6.pt\n"
          ]
        }
      ],
      "source": [
        "print(\"Now we are ready to train yolov5 model\")\n",
        "! ls"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "35e55eef",
      "metadata": {
        "papermill": {
          "duration": 0.008801,
          "end_time": "2022-06-29T17:55:37.203164",
          "exception": false,
          "start_time": "2022-06-29T17:55:37.194363",
          "status": "completed"
        },
        "tags": [],
        "id": "35e55eef"
      },
      "source": [
        "# Train yolov5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "7bf0cca1",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-29T17:55:37.222577Z",
          "iopub.status.busy": "2022-06-29T17:55:37.222110Z",
          "iopub.status.idle": "2022-06-29T17:55:37.895847Z",
          "shell.execute_reply": "2022-06-29T17:55:37.894709Z"
        },
        "papermill": {
          "duration": 0.686094,
          "end_time": "2022-06-29T17:55:37.898146",
          "exception": false,
          "start_time": "2022-06-29T17:55:37.212052",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7bf0cca1",
        "outputId": "f43a4e1b-361d-4b69-b0cc-0b8c0dc99953"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rm: cannot remove 'yolov5_train/feature_extraction*': No such file or directory\n"
          ]
        }
      ],
      "source": [
        "# Delete old results if exists\n",
        "wildcard = f\"{PROJECT_NAME}/feature_extraction*\"\n",
        "! rm -r $wildcard"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "c68ae7e8",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-29T17:55:37.918360Z",
          "iopub.status.busy": "2022-06-29T17:55:37.918036Z",
          "iopub.status.idle": "2022-06-29T18:46:22.790401Z",
          "shell.execute_reply": "2022-06-29T18:46:22.789182Z"
        },
        "papermill": {
          "duration": 3044.885428,
          "end_time": "2022-06-29T18:46:22.792944",
          "exception": false,
          "start_time": "2022-06-29T17:55:37.907516",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c68ae7e8",
        "outputId": "74bcae65-a360-4008-b9cf-cf6848152a3f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2024-03-07 07:27:16.019167: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-03-07 07:27:16.019251: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-03-07 07:27:16.020917: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5m6.pt, cfg=, data=data.yaml, hyp=yolov5/data/hyps/hyp.scratch-low.yaml, epochs=200, batch_size=32, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=yolov5/data/hyps, resume_evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=yolov5_train, name=feature_extraction, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[12], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest, ndjson_console=False, ndjson_file=False\n",
            "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
            "YOLOv5 🚀 v7.0-290-gb2ffe055 Python-3.10.12 torch-2.1.0+cu121 CPU\n",
            "\n",
            "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
            "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir yolov5_train', view at http://localhost:6006/\n",
            "Downloading https://ultralytics.com/assets/Arial.Unicode.ttf to /root/.config/Ultralytics/Arial.Unicode.ttf...\n",
            "100% 22.2M/22.2M [00:00<00:00, 155MB/s]\n",
            "Overriding model.yaml nc=80 with nc=20\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1      5280  models.common.Conv                      [3, 48, 6, 2, 2]              \n",
            "  1                -1  1     41664  models.common.Conv                      [48, 96, 3, 2]                \n",
            "  2                -1  2     65280  models.common.C3                        [96, 96, 2]                   \n",
            "  3                -1  1    166272  models.common.Conv                      [96, 192, 3, 2]               \n",
            "  4                -1  4    444672  models.common.C3                        [192, 192, 4]                 \n",
            "  5                -1  1    664320  models.common.Conv                      [192, 384, 3, 2]              \n",
            "  6                -1  6   2512896  models.common.C3                        [384, 384, 6]                 \n",
            "  7                -1  1   1991808  models.common.Conv                      [384, 576, 3, 2]              \n",
            "  8                -1  2   2327040  models.common.C3                        [576, 576, 2]                 \n",
            "  9                -1  1   3982848  models.common.Conv                      [576, 768, 3, 2]              \n",
            " 10                -1  2   4134912  models.common.C3                        [768, 768, 2]                 \n",
            " 11                -1  1   1476864  models.common.SPPF                      [768, 768, 5]                 \n",
            " 12                -1  1    443520  models.common.Conv                      [768, 576, 1, 1]              \n",
            " 13                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 14           [-1, 8]  1         0  models.common.Concat                    [1]                           \n",
            " 15                -1  2   2658816  models.common.C3                        [1152, 576, 2, False]         \n",
            " 16                -1  1    221952  models.common.Conv                      [576, 384, 1, 1]              \n",
            " 17                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 18           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
            " 19                -1  2   1182720  models.common.C3                        [768, 384, 2, False]          \n",
            " 20                -1  1     74112  models.common.Conv                      [384, 192, 1, 1]              \n",
            " 21                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 22           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
            " 23                -1  2    296448  models.common.C3                        [384, 192, 2, False]          \n",
            " 24                -1  1    332160  models.common.Conv                      [192, 192, 3, 2]              \n",
            " 25          [-1, 20]  1         0  models.common.Concat                    [1]                           \n",
            " 26                -1  2   1035264  models.common.C3                        [384, 384, 2, False]          \n",
            " 27                -1  1   1327872  models.common.Conv                      [384, 384, 3, 2]              \n",
            " 28          [-1, 16]  1         0  models.common.Concat                    [1]                           \n",
            " 29                -1  2   2437632  models.common.C3                        [768, 576, 2, False]          \n",
            " 30                -1  1   2987136  models.common.Conv                      [576, 576, 3, 2]              \n",
            " 31          [-1, 12]  1         0  models.common.Concat                    [1]                           \n",
            " 32                -1  2   4429824  models.common.C3                        [1152, 768, 2, False]         \n",
            " 33  [23, 26, 29, 32]  1    144300  models.yolo.Detect                      [20, [[19, 27, 44, 40, 38, 94], [96, 68, 86, 152, 180, 137], [140, 301, 303, 264, 238, 542], [436, 615, 739, 380, 925, 792]], [192, 384, 576, 768]]\n",
            "Model summary: 379 layers, 35385612 parameters, 35385612 gradients, 49.5 GFLOPs\n",
            "\n",
            "Transferred 619/627 items from yolov5m6.pt\n",
            "freezing model.0.conv.weight\n",
            "freezing model.0.bn.weight\n",
            "freezing model.0.bn.bias\n",
            "freezing model.1.conv.weight\n",
            "freezing model.1.bn.weight\n",
            "freezing model.1.bn.bias\n",
            "freezing model.2.cv1.conv.weight\n",
            "freezing model.2.cv1.bn.weight\n",
            "freezing model.2.cv1.bn.bias\n",
            "freezing model.2.cv2.conv.weight\n",
            "freezing model.2.cv2.bn.weight\n",
            "freezing model.2.cv2.bn.bias\n",
            "freezing model.2.cv3.conv.weight\n",
            "freezing model.2.cv3.bn.weight\n",
            "freezing model.2.cv3.bn.bias\n",
            "freezing model.2.m.0.cv1.conv.weight\n",
            "freezing model.2.m.0.cv1.bn.weight\n",
            "freezing model.2.m.0.cv1.bn.bias\n",
            "freezing model.2.m.0.cv2.conv.weight\n",
            "freezing model.2.m.0.cv2.bn.weight\n",
            "freezing model.2.m.0.cv2.bn.bias\n",
            "freezing model.2.m.1.cv1.conv.weight\n",
            "freezing model.2.m.1.cv1.bn.weight\n",
            "freezing model.2.m.1.cv1.bn.bias\n",
            "freezing model.2.m.1.cv2.conv.weight\n",
            "freezing model.2.m.1.cv2.bn.weight\n",
            "freezing model.2.m.1.cv2.bn.bias\n",
            "freezing model.3.conv.weight\n",
            "freezing model.3.bn.weight\n",
            "freezing model.3.bn.bias\n",
            "freezing model.4.cv1.conv.weight\n",
            "freezing model.4.cv1.bn.weight\n",
            "freezing model.4.cv1.bn.bias\n",
            "freezing model.4.cv2.conv.weight\n",
            "freezing model.4.cv2.bn.weight\n",
            "freezing model.4.cv2.bn.bias\n",
            "freezing model.4.cv3.conv.weight\n",
            "freezing model.4.cv3.bn.weight\n",
            "freezing model.4.cv3.bn.bias\n",
            "freezing model.4.m.0.cv1.conv.weight\n",
            "freezing model.4.m.0.cv1.bn.weight\n",
            "freezing model.4.m.0.cv1.bn.bias\n",
            "freezing model.4.m.0.cv2.conv.weight\n",
            "freezing model.4.m.0.cv2.bn.weight\n",
            "freezing model.4.m.0.cv2.bn.bias\n",
            "freezing model.4.m.1.cv1.conv.weight\n",
            "freezing model.4.m.1.cv1.bn.weight\n",
            "freezing model.4.m.1.cv1.bn.bias\n",
            "freezing model.4.m.1.cv2.conv.weight\n",
            "freezing model.4.m.1.cv2.bn.weight\n",
            "freezing model.4.m.1.cv2.bn.bias\n",
            "freezing model.4.m.2.cv1.conv.weight\n",
            "freezing model.4.m.2.cv1.bn.weight\n",
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            "freezing model.4.m.2.cv2.conv.weight\n",
            "freezing model.4.m.2.cv2.bn.weight\n",
            "freezing model.4.m.2.cv2.bn.bias\n",
            "freezing model.4.m.3.cv1.conv.weight\n",
            "freezing model.4.m.3.cv1.bn.weight\n",
            "freezing model.4.m.3.cv1.bn.bias\n",
            "freezing model.4.m.3.cv2.conv.weight\n",
            "freezing model.4.m.3.cv2.bn.weight\n",
            "freezing model.4.m.3.cv2.bn.bias\n",
            "freezing model.5.conv.weight\n",
            "freezing model.5.bn.weight\n",
            "freezing model.5.bn.bias\n",
            "freezing model.6.cv1.conv.weight\n",
            "freezing model.6.cv1.bn.weight\n",
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            "freezing model.6.cv2.conv.weight\n",
            "freezing model.6.cv2.bn.weight\n",
            "freezing model.6.cv2.bn.bias\n",
            "freezing model.6.cv3.conv.weight\n",
            "freezing model.6.cv3.bn.weight\n",
            "freezing model.6.cv3.bn.bias\n",
            "freezing model.6.m.0.cv1.conv.weight\n",
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            "freezing model.6.m.0.cv2.bn.weight\n",
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            "freezing model.6.m.3.cv1.conv.weight\n",
            "freezing model.6.m.3.cv1.bn.weight\n",
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            "freezing model.6.m.3.cv2.conv.weight\n",
            "freezing model.6.m.3.cv2.bn.weight\n",
            "freezing model.6.m.3.cv2.bn.bias\n",
            "freezing model.6.m.4.cv1.conv.weight\n",
            "freezing model.6.m.4.cv1.bn.weight\n",
            "freezing model.6.m.4.cv1.bn.bias\n",
            "freezing model.6.m.4.cv2.conv.weight\n",
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            "freezing model.6.m.4.cv2.bn.bias\n",
            "freezing model.6.m.5.cv1.conv.weight\n",
            "freezing model.6.m.5.cv1.bn.weight\n",
            "freezing model.6.m.5.cv1.bn.bias\n",
            "freezing model.6.m.5.cv2.conv.weight\n",
            "freezing model.6.m.5.cv2.bn.weight\n",
            "freezing model.6.m.5.cv2.bn.bias\n",
            "freezing model.7.conv.weight\n",
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            "freezing model.8.cv1.conv.weight\n",
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            "freezing model.8.cv1.bn.bias\n",
            "freezing model.8.cv2.conv.weight\n",
            "freezing model.8.cv2.bn.weight\n",
            "freezing model.8.cv2.bn.bias\n",
            "freezing model.8.cv3.conv.weight\n",
            "freezing model.8.cv3.bn.weight\n",
            "freezing model.8.cv3.bn.bias\n",
            "freezing model.8.m.0.cv1.conv.weight\n",
            "freezing model.8.m.0.cv1.bn.weight\n",
            "freezing model.8.m.0.cv1.bn.bias\n",
            "freezing model.8.m.0.cv2.conv.weight\n",
            "freezing model.8.m.0.cv2.bn.weight\n",
            "freezing model.8.m.0.cv2.bn.bias\n",
            "freezing model.8.m.1.cv1.conv.weight\n",
            "freezing model.8.m.1.cv1.bn.weight\n",
            "freezing model.8.m.1.cv1.bn.bias\n",
            "freezing model.8.m.1.cv2.conv.weight\n",
            "freezing model.8.m.1.cv2.bn.weight\n",
            "freezing model.8.m.1.cv2.bn.bias\n",
            "freezing model.9.conv.weight\n",
            "freezing model.9.bn.weight\n",
            "freezing model.9.bn.bias\n",
            "freezing model.10.cv1.conv.weight\n",
            "freezing model.10.cv1.bn.weight\n",
            "freezing model.10.cv1.bn.bias\n",
            "freezing model.10.cv2.conv.weight\n",
            "freezing model.10.cv2.bn.weight\n",
            "freezing model.10.cv2.bn.bias\n",
            "freezing model.10.cv3.conv.weight\n",
            "freezing model.10.cv3.bn.weight\n",
            "freezing model.10.cv3.bn.bias\n",
            "freezing model.10.m.0.cv1.conv.weight\n",
            "freezing model.10.m.0.cv1.bn.weight\n",
            "freezing model.10.m.0.cv1.bn.bias\n",
            "freezing model.10.m.0.cv2.conv.weight\n",
            "freezing model.10.m.0.cv2.bn.weight\n",
            "freezing model.10.m.0.cv2.bn.bias\n",
            "freezing model.10.m.1.cv1.conv.weight\n",
            "freezing model.10.m.1.cv1.bn.weight\n",
            "freezing model.10.m.1.cv1.bn.bias\n",
            "freezing model.10.m.1.cv2.conv.weight\n",
            "freezing model.10.m.1.cv2.bn.weight\n",
            "freezing model.10.m.1.cv2.bn.bias\n",
            "freezing model.11.cv1.conv.weight\n",
            "freezing model.11.cv1.bn.weight\n",
            "freezing model.11.cv1.bn.bias\n",
            "freezing model.11.cv2.conv.weight\n",
            "freezing model.11.cv2.bn.weight\n",
            "freezing model.11.cv2.bn.bias\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 103 weight(decay=0.0), 107 weight(decay=0.0005), 107 bias\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/train/labels... 12 images, 0 backgrounds, 0 corrupt: 100% 12/12 [00:00<00:00, 281.20it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train/images/63399d6b-50.jpg: 1 duplicate labels removed\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/train/labels.cache\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.0GB ram): 100% 12/12 [00:00<00:00, 87.55it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/valid/labels... 2 images, 0 backgrounds, 0 corrupt: 100% 2/2 [00:00<00:00, 75.44it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/valid/labels.cache\n",
            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.0GB ram): 100% 2/2 [00:00<00:00, 12.15it/s]\n",
            "\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m2.26 anchors/target, 0.998 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
            "Plotting labels to yolov5_train/feature_extraction/labels.jpg... \n",
            "Image sizes 640 train, 640 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1myolov5_train/feature_extraction\u001b[0m\n",
            "Starting training for 200 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      0/199         0G    0.06527    0.07906    0.04091       1342        640:   0% 0/1 [00:33<?, ?it/s]Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n",
            "\n",
            "100% 755k/755k [00:00<00:00, 18.0MB/s]\n",
            "      0/199         0G    0.06527    0.07906    0.04091       1342        640: 100% 1/1 [00:54<00:00, 54.45s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.30s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      1/199         0G    0.06421    0.05891    0.04141        814        640: 100% 1/1 [00:34<00:00, 34.30s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:07<00:00,  7.87s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      2/199         0G    0.06445    0.06345    0.04124        798        640: 100% 1/1 [00:28<00:00, 28.82s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:09<00:00,  9.15s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      3/199         0G    0.06481    0.05171    0.04112       1047        640: 100% 1/1 [00:30<00:00, 30.37s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.59s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      4/199         0G    0.06397    0.06623    0.04131        901        640: 100% 1/1 [00:28<00:00, 28.39s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.27s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      5/199         0G    0.06377    0.06389    0.04087        775        640: 100% 1/1 [00:28<00:00, 28.76s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.76s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      6/199         0G    0.06393    0.07836    0.04115       1300        640: 100% 1/1 [00:28<00:00, 28.68s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.43s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      7/199         0G    0.06322    0.07523    0.04102       1327        640: 100% 1/1 [00:29<00:00, 29.02s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.62s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      8/199         0G    0.06079    0.06418    0.04052        628        640: 100% 1/1 [00:28<00:00, 28.59s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.57s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      9/199         0G    0.06156    0.06864    0.04069        796        640: 100% 1/1 [00:28<00:00, 28.92s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.55s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     10/199         0G    0.06093    0.07726    0.04071        965        640: 100% 1/1 [00:28<00:00, 28.19s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.50s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     11/199         0G    0.06146    0.08651    0.04133       1211        640: 100% 1/1 [00:26<00:00, 26.98s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.60s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     12/199         0G    0.06047    0.07818    0.04077       1024        640: 100% 1/1 [00:28<00:00, 28.72s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.20s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     13/199         0G    0.06052    0.06836    0.04044        967        640: 100% 1/1 [00:28<00:00, 28.75s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.04s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     14/199         0G    0.05934    0.05296    0.04111        625        640: 100% 1/1 [00:31<00:00, 31.80s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.36s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     15/199         0G    0.05973    0.06052    0.04032       1288        640: 100% 1/1 [00:28<00:00, 28.59s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.95s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     16/199         0G    0.05888    0.06967     0.0405        890        640: 100% 1/1 [00:28<00:00, 28.30s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.43s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     17/199         0G    0.06049    0.06491    0.03996       1283        640: 100% 1/1 [00:27<00:00, 27.93s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.51s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     18/199         0G    0.05912    0.07524    0.04003       1085        640: 100% 1/1 [00:32<00:00, 32.08s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.60s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     19/199         0G    0.05652    0.05853    0.03953        762        640: 100% 1/1 [00:28<00:00, 28.35s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:05<00:00,  5.18s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     20/199         0G     0.0555    0.06214    0.03957        400        640: 100% 1/1 [00:28<00:00, 28.08s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.70s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     21/199         0G     0.0564      0.087    0.03969       1264        640: 100% 1/1 [00:28<00:00, 28.64s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.21s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     22/199         0G    0.05744     0.0679    0.03875       1175        640: 100% 1/1 [00:28<00:00, 28.19s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.02s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     23/199         0G    0.05619    0.06957    0.03893       1411        640: 100% 1/1 [00:28<00:00, 28.43s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.05s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     24/199         0G    0.05449    0.06528    0.03929        796        640: 100% 1/1 [00:28<00:00, 28.76s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.16s/it]\n",
            "                   all          2        129      0.624    0.00239    0.00301   0.000301\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     25/199         0G    0.05233    0.07204    0.03911        800        640: 100% 1/1 [00:28<00:00, 28.18s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.69s/it]\n",
            "                   all          2        129      0.571    0.00239    0.00351   0.000634\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     26/199         0G    0.05213    0.05804    0.03897        540        640: 100% 1/1 [00:28<00:00, 28.54s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.18s/it]\n",
            "                   all          2        129      0.622    0.00239    0.00335    0.00061\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     27/199         0G     0.0519    0.08797    0.03856       1000        640: 100% 1/1 [00:28<00:00, 28.04s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.49s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     28/199         0G    0.05178    0.07496    0.03868       1339        640: 100% 1/1 [00:27<00:00, 27.09s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:05<00:00,  5.50s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     29/199         0G    0.04959    0.08349    0.03824        964        640: 100% 1/1 [00:28<00:00, 28.22s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.19s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     30/199         0G    0.05039    0.06731    0.03798        946        640: 100% 1/1 [00:28<00:00, 28.12s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.95s/it]\n",
            "                   all          2        129     0.0147     0.0219    0.00887    0.00095\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     31/199         0G    0.04962    0.07958    0.03808        937        640: 100% 1/1 [00:30<00:00, 30.49s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.22s/it]\n",
            "                   all          2        129   0.000591    0.00439   0.000333   3.33e-05\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     32/199         0G     0.0485    0.06484    0.03782        656        640: 100% 1/1 [00:27<00:00, 27.93s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.61s/it]\n",
            "                   all          2        129     0.0102     0.0151     0.0063   0.000717\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     33/199         0G    0.04692    0.08682    0.03717       1017        640: 100% 1/1 [00:27<00:00, 27.71s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:05<00:00,  5.23s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     34/199         0G    0.04673    0.07487    0.03675        957        640: 100% 1/1 [00:29<00:00, 29.45s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.20s/it]\n",
            "                   all          2        129   0.000511    0.00239   0.000277   5.54e-05\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     35/199         0G    0.04643    0.08453    0.03643       1001        640: 100% 1/1 [00:36<00:00, 36.12s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.64s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     36/199         0G     0.0464    0.06582    0.03658        953        640: 100% 1/1 [00:28<00:00, 28.25s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.25s/it]\n",
            "                   all          2        129     0.0103     0.0172      0.008     0.0011\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     37/199         0G    0.04919    0.06957    0.03638        948        640: 100% 1/1 [00:28<00:00, 28.09s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:05<00:00,  5.77s/it]\n",
            "                   all          2        129          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     38/199         0G    0.04779    0.07536     0.0354        945        640: 100% 1/1 [00:28<00:00, 28.63s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.24s/it]\n",
            "                   all          2        129      0.426    0.00239   0.000977   0.000195\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     39/199         0G    0.04565    0.07696    0.03603       1165        640: 100% 1/1 [00:28<00:00, 28.14s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.49s/it]\n",
            "                   all          2        129    0.00953     0.0187     0.0055    0.00281\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     40/199         0G     0.0458     0.0673    0.03611        689        640: 100% 1/1 [00:27<00:00, 27.37s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.73s/it]\n",
            "                   all          2        129    0.00397    0.00793    0.00463   0.000647\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     41/199         0G     0.0467    0.06004    0.03468        788        640: 100% 1/1 [00:28<00:00, 28.68s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.19s/it]\n",
            "                   all          2        129     0.0165     0.0302     0.0116    0.00228\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     42/199         0G    0.04872    0.08151     0.0356        982        640: 100% 1/1 [00:27<00:00, 27.65s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:05<00:00,  5.77s/it]\n",
            "                   all          2        129      0.107    0.00277   0.000982   0.000295\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     43/199         0G    0.04288    0.07082    0.03464        967        640: 100% 1/1 [00:27<00:00, 27.83s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.19s/it]\n",
            "                   all          2        129    0.00923      0.023    0.00865    0.00227\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     44/199         0G      0.049    0.08691    0.03526       1176        640: 100% 1/1 [00:28<00:00, 28.53s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.15s/it]\n",
            "                   all          2        129     0.0547    0.00277   0.000854   0.000171\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     45/199         0G    0.04496    0.07811    0.03531        933        640: 100% 1/1 [00:27<00:00, 27.78s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:05<00:00,  5.88s/it]\n",
            "                   all          2        129      0.112     0.0127    0.00538    0.00155\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     46/199         0G    0.04389    0.06546    0.03384        968        640: 100% 1/1 [00:28<00:00, 28.47s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.13s/it]\n",
            "                   all          2        129    0.00684     0.0218    0.00515    0.00106\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     47/199         0G    0.04525    0.07211    0.03331        970        640: 100% 1/1 [00:27<00:00, 28.00s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.12s/it]\n",
            "                   all          2        129     0.0199     0.0339      0.015    0.00325\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     48/199         0G    0.04393    0.07092    0.03458        990        640: 100% 1/1 [00:28<00:00, 28.00s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.16s/it]\n",
            "                   all          2        129    0.00549     0.0214    0.00681    0.00139\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     49/199         0G    0.04286    0.07348     0.0351        865        640: 100% 1/1 [00:28<00:00, 28.68s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.04s/it]\n",
            "                   all          2        129    0.00878     0.0228    0.00774    0.00168\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     50/199         0G    0.04387    0.06612    0.03307        657        640: 100% 1/1 [00:27<00:00, 27.84s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:05<00:00,  5.73s/it]\n",
            "                   all          2        129    0.00878     0.0228    0.00774    0.00168\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     51/199         0G    0.04389    0.06717    0.03401        819        640: 100% 1/1 [00:28<00:00, 28.25s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.94s/it]\n",
            "                   all          2        129      0.108    0.00516    0.00111   0.000246\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     52/199         0G    0.04678     0.0576    0.03481        885        640: 100% 1/1 [00:27<00:00, 27.81s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:05<00:00,  5.32s/it]\n",
            "                   all          2        129      0.108    0.00516    0.00111   0.000246\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     53/199         0G    0.04711    0.05315    0.03418        918        640: 100% 1/1 [00:28<00:00, 28.40s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.75s/it]\n",
            "                   all          2        129      0.439    0.00516    0.00382    0.00102\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     54/199         0G    0.04703    0.06363    0.03404       1122        640: 100% 1/1 [00:28<00:00, 28.47s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.55s/it]\n",
            "                   all          2        129      0.439    0.00516    0.00382    0.00102\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     55/199         0G    0.04609    0.07203    0.03361        799        640: 100% 1/1 [00:28<00:00, 28.39s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.73s/it]\n",
            "                   all          2        129     0.0202     0.0644     0.0241     0.0059\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     56/199         0G    0.03682    0.08105    0.03393        853        640: 100% 1/1 [00:27<00:00, 27.79s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.94s/it]\n",
            "                   all          2        129     0.0202     0.0644     0.0241     0.0059\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     57/199         0G    0.03917    0.06372    0.03384        762        640: 100% 1/1 [00:28<00:00, 28.44s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.86s/it]\n",
            "                   all          2        129      0.274    0.00516    0.00475    0.00133\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     58/199         0G    0.04324    0.08053    0.03376       1103        640: 100% 1/1 [00:27<00:00, 27.61s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.86s/it]\n",
            "                   all          2        129      0.274    0.00516    0.00475    0.00133\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     59/199         0G    0.04469    0.06877    0.03387        923        640: 100% 1/1 [00:29<00:00, 29.48s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:05<00:00,  5.58s/it]\n",
            "                   all          2        129      0.107    0.00516    0.00105   0.000312\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     60/199         0G     0.0457    0.07316    0.03408       1103        640: 100% 1/1 [00:28<00:00, 28.58s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.90s/it]\n",
            "                   all          2        129      0.107    0.00516    0.00105   0.000312\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     61/199         0G    0.04594    0.07968    0.03415       1215        640: 100% 1/1 [00:29<00:00, 29.27s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.80s/it]\n",
            "                   all          2        129     0.0114      0.039     0.0108    0.00214\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     62/199         0G    0.03871     0.0565    0.03346        741        640: 100% 1/1 [00:28<00:00, 28.92s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:05<00:00,  5.02s/it]\n",
            "                   all          2        129     0.0114      0.039     0.0108    0.00214\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     63/199         0G     0.0393    0.05142    0.03431        775        640: 100% 1/1 [00:28<00:00, 28.67s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.81s/it]\n",
            "                   all          2        129    0.00379     0.0227    0.00341    0.00088\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     64/199         0G    0.05205      0.061    0.03382       1153        640: 100% 1/1 [00:27<00:00, 27.92s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.80s/it]\n",
            "                   all          2        129    0.00379     0.0227    0.00341    0.00088\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     65/199         0G    0.05035    0.06501    0.03297        729        640: 100% 1/1 [00:27<00:00, 27.89s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:05<00:00,  5.28s/it]\n",
            "                   all          2        129      0.011     0.0263    0.00708    0.00217\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     66/199         0G    0.05126    0.05511      0.034       1003        640: 100% 1/1 [00:28<00:00, 28.43s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.76s/it]\n",
            "                   all          2        129      0.011     0.0263    0.00708    0.00217\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     67/199         0G    0.04936    0.06376    0.03434        930        640: 100% 1/1 [00:28<00:00, 28.23s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.62s/it]\n",
            "                   all          2        129    0.00567     0.0152     0.0048    0.00048\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     68/199         0G    0.04468    0.06656    0.03258        626        640: 100% 1/1 [00:27<00:00, 27.42s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:05<00:00,  5.24s/it]\n",
            "                   all          2        129    0.00567     0.0152     0.0048    0.00048\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     69/199         0G    0.04524     0.0607    0.03371        848        640: 100% 1/1 [00:28<00:00, 28.59s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.63s/it]\n",
            "                   all          2        129      0.286    0.00516    0.00357   0.000933\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     70/199         0G     0.0394    0.05826    0.03289        856        640: 100% 1/1 [00:31<00:00, 31.93s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:05<00:00,  5.21s/it]\n",
            "                   all          2        129      0.286    0.00516    0.00357   0.000933\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     71/199         0G    0.04259    0.05663    0.03431        760        640: 100% 1/1 [00:28<00:00, 28.91s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.57s/it]\n",
            "                   all          2        129     0.0177     0.0333     0.0122    0.00291\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     72/199         0G    0.04043     0.1045    0.03296       1333        640: 100% 1/1 [00:28<00:00, 28.30s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.57s/it]\n",
            "                   all          2        129     0.0177     0.0333     0.0122    0.00291\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     73/199         0G    0.03693    0.06411    0.03322        678        640: 100% 1/1 [00:28<00:00, 28.20s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:05<00:00,  5.38s/it]\n",
            "                   all          2        129       0.11     0.0171    0.00336   0.000507\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     74/199         0G    0.04373    0.05678    0.03296        744        640: 100% 1/1 [00:29<00:00, 29.11s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.91s/it]\n",
            "                   all          2        129       0.11     0.0171    0.00336   0.000507\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     75/199         0G    0.04336     0.0648    0.03267        909        640: 100% 1/1 [00:29<00:00, 29.62s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.41s/it]\n",
            "                   all          2        129     0.0145     0.0382     0.0103    0.00313\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     76/199         0G    0.04588    0.05343    0.03242       1032        640: 100% 1/1 [00:27<00:00, 27.92s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.90s/it]\n",
            "                   all          2        129     0.0145     0.0382     0.0103    0.00313\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     77/199         0G    0.04461    0.07218    0.03327       1116        640: 100% 1/1 [00:28<00:00, 28.41s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.51s/it]\n",
            "                   all          2        129     0.0228     0.0643     0.0236     0.0058\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     78/199         0G    0.03879     0.0773    0.03362       1115        640: 100% 1/1 [00:28<00:00, 28.21s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.52s/it]\n",
            "                   all          2        129     0.0228     0.0643     0.0236     0.0058\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     79/199         0G    0.03753    0.06076    0.03251        740        640: 100% 1/1 [00:28<00:00, 28.07s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.47s/it]\n",
            "                   all          2        129      0.219    0.00755    0.00402   0.000576\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     80/199         0G    0.04648    0.06844    0.03292       1394        640: 100% 1/1 [00:26<00:00, 26.99s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.40s/it]\n",
            "                   all          2        129      0.219    0.00755    0.00402   0.000576\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     81/199         0G    0.04406    0.06799    0.03209        956        640: 100% 1/1 [00:28<00:00, 28.93s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.56s/it]\n",
            "                   all          2        129      0.163     0.0147    0.00444   0.000627\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     82/199         0G    0.04422    0.07532    0.03392       1442        640: 100% 1/1 [00:28<00:00, 28.40s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.36s/it]\n",
            "                   all          2        129      0.163     0.0147    0.00444   0.000627\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     83/199         0G    0.04379    0.08202      0.034       1361        640: 100% 1/1 [00:28<00:00, 28.50s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.14s/it]\n",
            "                   all          2        129     0.0114     0.0499     0.0109     0.0035\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     84/199         0G     0.0363    0.06945    0.03325        985        640: 100% 1/1 [00:28<00:00, 28.41s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.61s/it]\n",
            "                   all          2        129     0.0114     0.0499     0.0109     0.0035\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     85/199         0G    0.03513    0.07982    0.03354        947        640: 100% 1/1 [00:28<00:00, 28.17s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.42s/it]\n",
            "                   all          2        129      0.159    0.00277   0.000286   5.72e-05\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     86/199         0G    0.04254    0.06669    0.03209        902        640: 100% 1/1 [00:27<00:00, 27.53s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.96s/it]\n",
            "                   all          2        129      0.159    0.00277   0.000286   5.72e-05\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     87/199         0G    0.04171    0.06738    0.03267        886        640: 100% 1/1 [00:30<00:00, 30.57s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.24s/it]\n",
            "                   all          2        129    0.00691     0.0292    0.00613   0.000963\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     88/199         0G    0.03884    0.07865    0.03305        855        640: 100% 1/1 [00:31<00:00, 31.87s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.32s/it]\n",
            "                   all          2        129    0.00691     0.0292    0.00613   0.000963\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     89/199         0G     0.0426    0.07341     0.0318       1266        640: 100% 1/1 [00:28<00:00, 28.93s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.27s/it]\n",
            "                   all          2        129    0.00848     0.0307     0.0111    0.00253\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     90/199         0G    0.04155    0.09079    0.03338       1460        640: 100% 1/1 [00:28<00:00, 28.44s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.32s/it]\n",
            "                   all          2        129    0.00848     0.0307     0.0111    0.00253\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     91/199         0G    0.03805    0.06539    0.03266        829        640: 100% 1/1 [00:28<00:00, 28.19s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.73s/it]\n",
            "                   all          2        129     0.0189     0.0682     0.0235    0.00783\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     92/199         0G    0.04074    0.07031     0.0316       1596        640: 100% 1/1 [00:28<00:00, 28.60s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.40s/it]\n",
            "                   all          2        129     0.0189     0.0682     0.0235    0.00783\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     93/199         0G     0.0371    0.08075    0.03246       1205        640: 100% 1/1 [00:28<00:00, 28.25s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.57s/it]\n",
            "                   all          2        129     0.0145     0.0661      0.017    0.00406\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     94/199         0G    0.03981    0.09455    0.03342       1202        640: 100% 1/1 [00:27<00:00, 27.89s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  5.00s/it]\n",
            "                   all          2        129     0.0145     0.0661      0.017    0.00406\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     95/199         0G    0.04162    0.05951    0.03116        826        640: 100% 1/1 [00:28<00:00, 28.46s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.37s/it]\n",
            "                   all          2        129    0.00598     0.0289    0.00463   0.000531\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     96/199         0G    0.03965    0.06703    0.03406        719        640: 100% 1/1 [00:28<00:00, 28.00s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.33s/it]\n",
            "                   all          2        129    0.00598     0.0289    0.00463   0.000531\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     97/199         0G    0.03952    0.07893     0.0323        998        640: 100% 1/1 [00:28<00:00, 28.26s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.59s/it]\n",
            "                   all          2        129     0.0198     0.0549     0.0184      0.005\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     98/199         0G      0.039    0.07806     0.0333       1262        640: 100% 1/1 [00:27<00:00, 27.56s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.64s/it]\n",
            "                   all          2        129     0.0198     0.0549     0.0184      0.005\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "     99/199         0G      0.039    0.07956    0.03244       1328        640: 100% 1/1 [00:28<00:00, 28.55s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.47s/it]\n",
            "                   all          2        129     0.0105     0.0543     0.0131    0.00295\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    100/199         0G    0.04002    0.07327    0.03325       1147        640: 100% 1/1 [00:28<00:00, 28.29s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.46s/it]\n",
            "                   all          2        129     0.0105     0.0543     0.0131    0.00295\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    101/199         0G    0.03843    0.07225    0.03167        657        640: 100% 1/1 [00:28<00:00, 28.01s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.62s/it]\n",
            "                   all          2        129    0.00197     0.0103    0.00119    0.00033\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    102/199         0G    0.04123    0.07825    0.03225       1360        640: 100% 1/1 [00:28<00:00, 28.25s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.43s/it]\n",
            "                   all          2        129    0.00197     0.0103    0.00119    0.00033\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    103/199         0G    0.04393    0.07388    0.03336       1185        640: 100% 1/1 [00:28<00:00, 28.51s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.23s/it]\n",
            "                   all          2        129     0.0441     0.0868     0.0452     0.0112\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    104/199         0G    0.04019    0.06536    0.03372        888        640: 100% 1/1 [00:27<00:00, 27.63s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.54s/it]\n",
            "                   all          2        129     0.0441     0.0868     0.0452     0.0112\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    105/199         0G    0.04255    0.05619    0.03286       1072        640: 100% 1/1 [00:28<00:00, 28.70s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.18s/it]\n",
            "                   all          2        129     0.0406      0.095     0.0446     0.0151\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    106/199         0G    0.03739    0.05161    0.03329        622        640: 100% 1/1 [00:33<00:00, 33.34s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.54s/it]\n",
            "                   all          2        129     0.0406      0.095     0.0446     0.0151\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    107/199         0G    0.03896    0.08005     0.0329       1509        640: 100% 1/1 [00:28<00:00, 28.63s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.34s/it]\n",
            "                   all          2        129    0.00982      0.043     0.0111      0.002\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    108/199         0G    0.03551    0.06666    0.03222        953        640: 100% 1/1 [00:27<00:00, 27.92s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.95s/it]\n",
            "                   all          2        129    0.00982      0.043     0.0111      0.002\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    109/199         0G    0.03545     0.0811    0.03344       1224        640: 100% 1/1 [00:27<00:00, 27.49s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.14s/it]\n",
            "                   all          2        129     0.0689     0.0503      0.044    0.00963\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    110/199         0G    0.03962    0.06697    0.03153       1262        640: 100% 1/1 [00:28<00:00, 28.70s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.21s/it]\n",
            "                   all          2        129     0.0689     0.0503      0.044    0.00963\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    111/199         0G    0.03826    0.05915    0.03227        905        640: 100% 1/1 [00:28<00:00, 28.35s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.21s/it]\n",
            "                   all          2        129     0.0353      0.157     0.0415     0.0142\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    112/199         0G    0.03695    0.05629    0.03291        825        640: 100% 1/1 [00:27<00:00, 27.58s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.66s/it]\n",
            "                   all          2        129     0.0403     0.0818     0.0377     0.0105\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    113/199         0G    0.03601    0.06754    0.03351        987        640: 100% 1/1 [00:28<00:00, 28.59s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.20s/it]\n",
            "                   all          2        129     0.0312      0.131     0.0392      0.011\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    114/199         0G     0.0346     0.0829    0.03269       1194        640: 100% 1/1 [00:28<00:00, 28.15s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.20s/it]\n",
            "                   all          2        129     0.0312      0.131     0.0392      0.011\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    115/199         0G    0.03885    0.07279    0.03265       1374        640: 100% 1/1 [00:29<00:00, 29.44s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.93s/it]\n",
            "                   all          2        129     0.0551     0.0977     0.0454     0.0157\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    116/199         0G    0.03719    0.05171    0.03205       1112        640: 100% 1/1 [00:28<00:00, 28.37s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.19s/it]\n",
            "                   all          2        129     0.0368      0.173     0.0491     0.0199\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    117/199         0G    0.03573    0.07421    0.03269       1130        640: 100% 1/1 [00:28<00:00, 28.11s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.68s/it]\n",
            "                   all          2        129     0.0627     0.0524     0.0415    0.00863\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    118/199         0G    0.03119    0.06244    0.03168        831        640: 100% 1/1 [00:28<00:00, 28.14s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.24s/it]\n",
            "                   all          2        129     0.0302      0.136     0.0386     0.0113\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    119/199         0G    0.03527    0.07252    0.03278       1065        640: 100% 1/1 [00:29<00:00, 29.45s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.22s/it]\n",
            "                   all          2        129     0.0305      0.143     0.0371     0.0145\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    120/199         0G    0.03778    0.07274    0.03332       1334        640: 100% 1/1 [00:27<00:00, 27.89s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.74s/it]\n",
            "                   all          2        129     0.0746     0.0944     0.0588     0.0191\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    121/199         0G    0.03746    0.07029    0.03177       1110        640: 100% 1/1 [00:28<00:00, 28.54s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.18s/it]\n",
            "                   all          2        129      0.034      0.159     0.0427     0.0155\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    122/199         0G    0.03599    0.06248    0.03315       1221        640: 100% 1/1 [00:27<00:00, 27.98s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.16s/it]\n",
            "                   all          2        129      0.034      0.159     0.0427     0.0155\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    123/199         0G    0.03812    0.07281    0.03225       1328        640: 100% 1/1 [00:28<00:00, 28.05s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.50s/it]\n",
            "                   all          2        129     0.0364     0.0931     0.0381      0.013\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    124/199         0G    0.03329    0.05806    0.03371        885        640: 100% 1/1 [00:32<00:00, 32.67s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.18s/it]\n",
            "                   all          2        129     0.0363      0.168     0.0532     0.0207\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    125/199         0G    0.03091     0.0661    0.03315        858        640: 100% 1/1 [00:28<00:00, 28.42s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.33s/it]\n",
            "                   all          2        129      0.031     0.0342     0.0257    0.00306\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    126/199         0G    0.03402    0.06228    0.03174       1092        640: 100% 1/1 [00:28<00:00, 28.25s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.30s/it]\n",
            "                   all          2        129     0.0266      0.109     0.0318    0.00667\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    127/199         0G    0.03816    0.05971    0.03312       1420        640: 100% 1/1 [00:28<00:00, 28.69s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.05s/it]\n",
            "                   all          2        129     0.0374      0.176     0.0441     0.0162\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    128/199         0G    0.03061    0.06587    0.03319       1031        640: 100% 1/1 [00:28<00:00, 28.08s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.07s/it]\n",
            "                   all          2        129     0.0374      0.176     0.0441     0.0162\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    129/199         0G    0.03144    0.05041    0.03222        803        640: 100% 1/1 [00:27<00:00, 27.57s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.45s/it]\n",
            "                   all          2        129     0.0343      0.094     0.0405     0.0129\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    130/199         0G    0.03578    0.07576    0.03264       1362        640: 100% 1/1 [00:28<00:00, 28.33s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.00s/it]\n",
            "                   all          2        129     0.0325      0.156     0.0386     0.0146\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    131/199         0G    0.02984    0.05913    0.03138        750        640: 100% 1/1 [00:28<00:00, 28.08s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.11s/it]\n",
            "                   all          2        129     0.0246      0.122      0.029    0.00914\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    132/199         0G    0.03235    0.05708    0.03305        676        640: 100% 1/1 [00:27<00:00, 27.85s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.05s/it]\n",
            "                   all          2        129     0.0246      0.122      0.029    0.00914\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    133/199         0G     0.0338    0.05565    0.03246        859        640: 100% 1/1 [00:27<00:00, 27.42s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.57s/it]\n",
            "                   all          2        129     0.0432     0.0821     0.0402     0.0116\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    134/199         0G    0.03059    0.09136    0.03271       1257        640: 100% 1/1 [00:28<00:00, 28.47s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.09s/it]\n",
            "                   all          2        129     0.0299       0.14     0.0357     0.0146\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    135/199         0G    0.03106    0.08851     0.0324        990        640: 100% 1/1 [00:28<00:00, 28.31s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.15s/it]\n",
            "                   all          2        129     0.0196     0.0946     0.0294    0.00988\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    136/199         0G     0.0305    0.05445    0.03251        854        640: 100% 1/1 [00:27<00:00, 27.88s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.21s/it]\n",
            "                   all          2        129     0.0338     0.0501     0.0292    0.00865\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    137/199         0G    0.02674    0.07353    0.03202        923        640: 100% 1/1 [00:27<00:00, 27.70s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.27s/it]\n",
            "                   all          2        129     0.0279      0.125     0.0327     0.0146\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    138/199         0G    0.03005    0.04944    0.03146        593        640: 100% 1/1 [00:28<00:00, 28.31s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.19s/it]\n",
            "                   all          2        129     0.0279      0.125     0.0327     0.0146\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    139/199         0G    0.03128    0.06886    0.03174        798        640: 100% 1/1 [00:28<00:00, 28.21s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.19s/it]\n",
            "                   all          2        129     0.0657     0.0676     0.0442     0.0193\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    140/199         0G    0.02981    0.05864    0.03298       1020        640: 100% 1/1 [00:27<00:00, 27.82s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.90s/it]\n",
            "                   all          2        129     0.0657     0.0676     0.0442     0.0193\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    141/199         0G    0.02957    0.05956    0.03178       1031        640: 100% 1/1 [00:27<00:00, 27.07s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.21s/it]\n",
            "                   all          2        129     0.0304      0.143      0.038      0.016\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    142/199         0G    0.02963    0.06424    0.03269        781        640: 100% 1/1 [00:28<00:00, 28.22s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:05<00:00,  5.62s/it]\n",
            "                   all          2        129     0.0714     0.0854     0.0508     0.0226\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    143/199         0G    0.03141    0.04475    0.03194        617        640: 100% 1/1 [00:28<00:00, 28.65s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.11s/it]\n",
            "                   all          2        129      0.033      0.155     0.0419     0.0167\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    144/199         0G    0.03535    0.05566    0.03193       1004        640: 100% 1/1 [00:29<00:00, 29.91s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.09s/it]\n",
            "                   all          2        129      0.033      0.155     0.0419     0.0167\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    145/199         0G    0.03567    0.06609    0.03275       1135        640: 100% 1/1 [00:27<00:00, 27.48s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.36s/it]\n",
            "                   all          2        129     0.0709     0.0862      0.051     0.0226\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    146/199         0G    0.03124    0.06838    0.03121        795        640: 100% 1/1 [00:28<00:00, 28.46s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.04s/it]\n",
            "                   all          2        129     0.0378       0.17      0.046     0.0239\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    147/199         0G    0.03144    0.06486    0.03302        956        640: 100% 1/1 [00:28<00:00, 28.50s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.09s/it]\n",
            "                   all          2        129     0.0357       0.16     0.0471     0.0219\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    148/199         0G    0.02829    0.05788    0.03206        990        640: 100% 1/1 [00:27<00:00, 27.96s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.04s/it]\n",
            "                   all          2        129     0.0666     0.0759     0.0477     0.0207\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    149/199         0G    0.02821    0.06671    0.03198        903        640: 100% 1/1 [00:28<00:00, 28.50s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.10s/it]\n",
            "                   all          2        129     0.0343      0.158     0.0442      0.022\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    150/199         0G    0.02555     0.0591    0.03149        672        640: 100% 1/1 [00:27<00:00, 27.62s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.12s/it]\n",
            "                   all          2        129     0.0343      0.158     0.0442      0.022\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    151/199         0G    0.03236    0.06565    0.03259       1226        640: 100% 1/1 [00:28<00:00, 28.44s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.09s/it]\n",
            "                   all          2        129     0.0369      0.163     0.0469     0.0229\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    152/199         0G    0.02493     0.0621    0.03215       1044        640: 100% 1/1 [00:27<00:00, 27.75s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.69s/it]\n",
            "                   all          2        129     0.0369      0.163     0.0469     0.0229\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    153/199         0G    0.02809    0.05128    0.03333       1058        640: 100% 1/1 [00:28<00:00, 28.58s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.06s/it]\n",
            "                   all          2        129     0.0395       0.18     0.0561      0.025\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    154/199         0G    0.03071    0.06965     0.0319       1103        640: 100% 1/1 [00:27<00:00, 27.76s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.84s/it]\n",
            "                   all          2        129     0.0395       0.18     0.0561      0.025\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    155/199         0G    0.02884    0.05855     0.0323        829        640: 100% 1/1 [00:29<00:00, 29.05s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.05s/it]\n",
            "                   all          2        129      0.037      0.166     0.0479     0.0216\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    156/199         0G    0.02944    0.06032    0.03179       1030        640: 100% 1/1 [00:28<00:00, 28.27s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.00s/it]\n",
            "                   all          2        129      0.037      0.166     0.0479     0.0216\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    157/199         0G     0.0274     0.0511      0.032        983        640: 100% 1/1 [00:27<00:00, 27.62s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.56s/it]\n",
            "                   all          2        129     0.0383      0.173     0.0536     0.0238\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    158/199         0G    0.03053     0.0613    0.03177       1036        640: 100% 1/1 [00:26<00:00, 26.83s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.30s/it]\n",
            "                   all          2        129     0.0383      0.173     0.0536     0.0238\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    159/199         0G    0.03114    0.07534    0.03256       1113        640: 100% 1/1 [00:28<00:00, 28.09s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:02<00:00,  2.96s/it]\n",
            "                   all          2        129     0.0373      0.166     0.0536     0.0238\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    160/199         0G    0.02784    0.06162    0.03212        974        640: 100% 1/1 [00:28<00:00, 28.86s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:06<00:00,  6.45s/it]\n",
            "                   all          2        129     0.0373      0.166     0.0536     0.0238\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    161/199         0G    0.02734    0.05815    0.03225        875        640: 100% 1/1 [00:28<00:00, 28.95s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:02<00:00,  2.98s/it]\n",
            "                   all          2        129     0.0358      0.158     0.0519      0.023\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    162/199         0G    0.02855    0.06609    0.03265        871        640: 100% 1/1 [00:28<00:00, 28.37s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.05s/it]\n",
            "                   all          2        129     0.0358      0.158     0.0519      0.023\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    163/199         0G    0.03065    0.07938    0.03178       1318        640: 100% 1/1 [00:28<00:00, 28.15s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.40s/it]\n",
            "                   all          2        129      0.068     0.0849     0.0498     0.0185\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    164/199         0G     0.0305    0.05504    0.03184       1580        640: 100% 1/1 [00:26<00:00, 26.84s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.44s/it]\n",
            "                   all          2        129     0.0386      0.169     0.0529     0.0236\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    165/199         0G    0.02711    0.07728    0.03177       1186        640: 100% 1/1 [00:28<00:00, 28.95s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.03s/it]\n",
            "                   all          2        129     0.0386      0.166      0.052     0.0237\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    166/199         0G    0.02555    0.06944    0.03258       1071        640: 100% 1/1 [00:27<00:00, 27.80s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.02s/it]\n",
            "                   all          2        129     0.0386      0.166      0.052     0.0237\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    167/199         0G    0.02457    0.05072    0.03164        644        640: 100% 1/1 [00:27<00:00, 27.91s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.29s/it]\n",
            "                   all          2        129     0.0672     0.0801     0.0492     0.0178\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    168/199         0G    0.02936    0.06582     0.0326        986        640: 100% 1/1 [00:27<00:00, 27.32s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.18s/it]\n",
            "                   all          2        129     0.0369      0.164     0.0522     0.0242\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    169/199         0G    0.02909    0.07565    0.03157       1286        640: 100% 1/1 [00:29<00:00, 29.42s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.21s/it]\n",
            "                   all          2        129     0.0352      0.166     0.0527     0.0253\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    170/199         0G    0.02603    0.06533    0.03239        891        640: 100% 1/1 [00:27<00:00, 27.89s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.01s/it]\n",
            "                   all          2        129     0.0352      0.166     0.0527     0.0253\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    171/199         0G    0.02703     0.0527     0.0318        701        640: 100% 1/1 [00:27<00:00, 27.91s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.67s/it]\n",
            "                   all          2        129     0.0688     0.0876     0.0508     0.0186\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    172/199         0G    0.02482    0.05942    0.03166        792        640: 100% 1/1 [00:26<00:00, 26.87s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.15s/it]\n",
            "                   all          2        129     0.0387      0.172     0.0484     0.0228\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    173/199         0G     0.0252    0.04784    0.03204        762        640: 100% 1/1 [00:29<00:00, 29.68s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.02s/it]\n",
            "                   all          2        129     0.0387      0.172     0.0509      0.022\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    174/199         0G    0.02463    0.06528    0.03196        987        640: 100% 1/1 [00:27<00:00, 27.73s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.02s/it]\n",
            "                   all          2        129     0.0387      0.172     0.0509      0.022\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    175/199         0G    0.02349    0.05232    0.03105        720        640: 100% 1/1 [00:27<00:00, 27.72s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.94s/it]\n",
            "                   all          2        129     0.0399      0.173     0.0503     0.0219\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    176/199         0G    0.02523    0.05803    0.03147       1024        640: 100% 1/1 [00:29<00:00, 29.31s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:02<00:00,  2.98s/it]\n",
            "                   all          2        129     0.0399      0.173     0.0503     0.0219\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    177/199         0G    0.02737    0.06122    0.03357        845        640: 100% 1/1 [00:28<00:00, 28.59s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:02<00:00,  2.95s/it]\n",
            "                   all          2        129     0.0391      0.162     0.0472     0.0199\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    178/199         0G    0.02455    0.05882    0.03152        899        640: 100% 1/1 [00:27<00:00, 27.94s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:02<00:00,  2.99s/it]\n",
            "                   all          2        129     0.0391      0.162     0.0472     0.0199\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    179/199         0G    0.02646    0.05189    0.03195        873        640: 100% 1/1 [00:33<00:00, 33.11s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:02<00:00,  2.97s/it]\n",
            "                   all          2        129     0.0364      0.159     0.0466     0.0219\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    180/199         0G    0.02837    0.05851    0.03211       1121        640: 100% 1/1 [00:28<00:00, 28.61s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:02<00:00,  2.95s/it]\n",
            "                   all          2        129     0.0364      0.159     0.0466     0.0219\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    181/199         0G    0.02732    0.07605    0.03218       1320        640: 100% 1/1 [00:28<00:00, 28.20s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.46s/it]\n",
            "                   all          2        129      0.038      0.162      0.046     0.0223\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    182/199         0G    0.02662    0.06592    0.03265       1095        640: 100% 1/1 [00:26<00:00, 26.72s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.36s/it]\n",
            "                   all          2        129      0.038      0.162      0.046     0.0223\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    183/199         0G    0.02696    0.06242     0.0318        751        640: 100% 1/1 [00:28<00:00, 28.83s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:02<00:00,  3.00s/it]\n",
            "                   all          2        129     0.0367      0.155     0.0453     0.0227\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    184/199         0G    0.02416    0.06518    0.03147        729        640: 100% 1/1 [00:28<00:00, 28.36s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:02<00:00,  2.97s/it]\n",
            "                   all          2        129     0.0367      0.155     0.0453     0.0227\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    185/199         0G    0.02351     0.0657     0.0312        884        640: 100% 1/1 [00:27<00:00, 27.80s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.16s/it]\n",
            "                   all          2        129     0.0339       0.14     0.0427     0.0212\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    186/199         0G     0.0263    0.06902    0.03265        796        640: 100% 1/1 [00:27<00:00, 27.00s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:04<00:00,  4.44s/it]\n",
            "                   all          2        129     0.0339       0.14     0.0427     0.0212\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    187/199         0G    0.02629    0.07988    0.03165       1155        640: 100% 1/1 [00:28<00:00, 28.86s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:02<00:00,  2.98s/it]\n",
            "                   all          2        129     0.0341       0.14      0.042     0.0223\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    188/199         0G    0.02822    0.06648    0.03184       1091        640: 100% 1/1 [00:28<00:00, 28.40s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:02<00:00,  2.98s/it]\n",
            "                   all          2        129     0.0341       0.14      0.042     0.0223\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    189/199         0G    0.02801    0.06477    0.03258       1144        640: 100% 1/1 [00:27<00:00, 27.97s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.63s/it]\n",
            "                   all          2        129     0.0345      0.142     0.0445     0.0236\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    190/199         0G    0.02507     0.0592    0.03258       1139        640: 100% 1/1 [00:27<00:00, 27.55s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.81s/it]\n",
            "                   all          2        129     0.0345      0.142     0.0445     0.0236\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    191/199         0G    0.02782    0.06543    0.03307       1312        640: 100% 1/1 [00:29<00:00, 29.72s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:02<00:00,  2.98s/it]\n",
            "                   all          2        129      0.035      0.149     0.0457     0.0239\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    192/199         0G    0.02445    0.06673    0.03248       1037        640: 100% 1/1 [00:27<00:00, 27.84s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:02<00:00,  2.98s/it]\n",
            "                   all          2        129      0.035      0.149     0.0457     0.0239\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    193/199         0G    0.02442    0.06845    0.03152        806        640: 100% 1/1 [00:27<00:00, 27.99s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0% 0/1 [00:00<?, ?it/s]WARNING ⚠️ NMS time limit 0.600s exceeded\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.79s/it]\n",
            "                   all          2        129     0.0352      0.149     0.0456     0.0238\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    194/199         0G    0.02555    0.06462    0.03132       1229        640: 100% 1/1 [00:27<00:00, 27.56s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.49s/it]\n",
            "                   all          2        129     0.0352      0.149     0.0456     0.0238\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    195/199         0G    0.02442    0.05548    0.03099        990        640: 100% 1/1 [00:28<00:00, 28.77s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:02<00:00,  2.99s/it]\n",
            "                   all          2        129     0.0345      0.149     0.0451      0.024\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    196/199         0G    0.02422    0.08374    0.03225       1268        640: 100% 1/1 [00:28<00:00, 28.25s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:02<00:00,  2.99s/it]\n",
            "                   all          2        129     0.0345      0.149     0.0451      0.024\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    197/199         0G    0.02466    0.05541    0.03098       1243        640: 100% 1/1 [00:33<00:00, 33.17s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:03<00:00,  3.14s/it]\n",
            "                   all          2        129     0.0343      0.149     0.0459     0.0236\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    198/199         0G    0.02396    0.06125    0.03304        729        640: 100% 1/1 [00:28<00:00, 28.49s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:02<00:00,  2.99s/it]\n",
            "                   all          2        129     0.0343      0.149     0.0459     0.0236\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "    199/199         0G    0.02699    0.05969    0.03174       1205        640: 100% 1/1 [00:28<00:00, 28.17s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:02<00:00,  2.97s/it]\n",
            "                   all          2        129     0.0353      0.152     0.0462     0.0237\n",
            "\n",
            "200 epochs completed in 1.905 hours.\n",
            "Optimizer stripped from yolov5_train/feature_extraction/weights/last.pt, 71.4MB\n",
            "Optimizer stripped from yolov5_train/feature_extraction/weights/best.pt, 71.4MB\n",
            "\n",
            "Validating yolov5_train/feature_extraction/weights/best.pt...\n",
            "Fusing layers... \n",
            "Model summary: 276 layers, 35358588 parameters, 0 gradients, 49.1 GFLOPs\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:02<00:00,  2.99s/it]\n",
            "                   all          2        129     0.0395       0.18      0.056      0.025\n",
            "                     0          2         19      0.103      0.789      0.221      0.107\n",
            "                     1          2         17      0.092      0.471        0.2     0.0567\n",
            "                     2          2         22        0.1      0.682      0.214     0.0918\n",
            "                     3          2         10     0.0556        0.1     0.0388     0.0233\n",
            "                     4          2          7          0          0          0          0\n",
            "                     5          2         12     0.0513      0.167     0.0491     0.0241\n",
            "                     6          2          4      0.188       0.75      0.196      0.103\n",
            "                     7          2          6          0          0          0          0\n",
            "                     8          2          8     0.0435      0.125     0.0607     0.0243\n",
            "                     9          2          6      0.118      0.333     0.0837     0.0454\n",
            "                     А          2          1          0          0          0          0\n",
            "                     В          2          1          0          0          0          0\n",
            "                     Г          2          2          0          0          0          0\n",
            "                     К          2          3          0          0          0          0\n",
            "                     О          2          1          0          0          0          0\n",
            "                     П          2          1          0          0          0          0\n",
            "                     С          2          2          0          0          0          0\n",
            "                     Т          2          3          0          0          0          0\n",
            "                     У          2          4          0          0          0          0\n",
            "Results saved to \u001b[1myolov5_train/feature_extraction\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "! python yolov5/train.py --batch $TRAIN_BATCH --epochs $TRAIN_EPOCHS --data \"data.yaml\" --weights $BASE_MODEL --project $PROJECT_NAME --name 'feature_extraction' --cache --freeze 12"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r /content/yolov5_train.zip /content/yolov5_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XtcQd601O2m_",
        "outputId": "2d6e3fdf-ac93-4197-9bb3-2f40feb9de8e"
      },
      "id": "XtcQd601O2m_",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: content/yolov5_train/ (stored 0%)\n",
            "  adding: content/yolov5_train/feature_extraction/ (stored 0%)\n",
            "  adding: content/yolov5_train/feature_extraction/weights/ (stored 0%)\n",
            "  adding: content/yolov5_train/feature_extraction/weights/best.pt (deflated 8%)\n",
            "  adding: content/yolov5_train/feature_extraction/weights/last.pt (deflated 8%)\n",
            "  adding: content/yolov5_train/feature_extraction/R_curve.png (deflated 13%)\n",
            "  adding: content/yolov5_train/feature_extraction/PR_curve.png (deflated 12%)\n",
            "  adding: content/yolov5_train/feature_extraction/confusion_matrix.png (deflated 26%)\n",
            "  adding: content/yolov5_train/feature_extraction/results.png (deflated 6%)\n",
            "  adding: content/yolov5_train/feature_extraction/events.out.tfevents.1709796438.b031a8e4c260.6646.0 (deflated 41%)\n",
            "  adding: content/yolov5_train/feature_extraction/F1_curve.png (deflated 13%)\n",
            "  adding: content/yolov5_train/feature_extraction/train_batch1.jpg (deflated 18%)\n",
            "  adding: content/yolov5_train/feature_extraction/val_batch0_pred.jpg (deflated 46%)\n",
            "  adding: content/yolov5_train/feature_extraction/train_batch0.jpg (deflated 13%)\n",
            "  adding: content/yolov5_train/feature_extraction/labels_correlogram.jpg (deflated 44%)\n",
            "  adding: content/yolov5_train/feature_extraction/val_batch0_labels.jpg (deflated 39%)\n",
            "  adding: content/yolov5_train/feature_extraction/opt.yaml (deflated 50%)\n",
            "  adding: content/yolov5_train/feature_extraction/labels.jpg (deflated 36%)\n",
            "  adding: content/yolov5_train/feature_extraction/P_curve.png (deflated 8%)\n",
            "  adding: content/yolov5_train/feature_extraction/hyp.yaml (deflated 45%)\n",
            "  adding: content/yolov5_train/feature_extraction/results.csv (deflated 86%)\n",
            "  adding: content/yolov5_train/feature_extraction/train_batch2.jpg (deflated 19%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2e9fc781",
      "metadata": {
        "papermill": {
          "duration": 0.324157,
          "end_time": "2022-06-29T18:46:23.388631",
          "exception": false,
          "start_time": "2022-06-29T18:46:23.064474",
          "status": "completed"
        },
        "tags": [],
        "id": "2e9fc781"
      },
      "source": [
        "# Validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "e0d78410",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-29T18:46:23.926679Z",
          "iopub.status.busy": "2022-06-29T18:46:23.925905Z",
          "iopub.status.idle": "2022-06-29T18:46:24.673153Z",
          "shell.execute_reply": "2022-06-29T18:46:24.672031Z"
        },
        "papermill": {
          "duration": 1.017769,
          "end_time": "2022-06-29T18:46:24.675605",
          "exception": false,
          "start_time": "2022-06-29T18:46:23.657836",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e0d78410",
        "outputId": "3d7e9cd9-15a8-4456-a587-3cec414a73e5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rm: cannot remove 'yolov5_train/validation_on_test_data*': No such file or directory\n"
          ]
        }
      ],
      "source": [
        "# Delete old results\n",
        "wildcard = f\"{PROJECT_NAME}/validation_on_test_data*\"\n",
        "! rm -r $wildcard"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "e362a5f9",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-29T18:46:25.215959Z",
          "iopub.status.busy": "2022-06-29T18:46:25.215577Z",
          "iopub.status.idle": "2022-06-29T18:46:42.498236Z",
          "shell.execute_reply": "2022-06-29T18:46:42.497127Z"
        },
        "papermill": {
          "duration": 17.556236,
          "end_time": "2022-06-29T18:46:42.500659",
          "exception": false,
          "start_time": "2022-06-29T18:46:24.944423",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e362a5f9",
        "outputId": "7024efbb-b1bc-4cd8-8c80-cdb8a85bce3a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mval: \u001b[0mdata=data.yaml, weights=['yolov5_train/feature_extraction/weights/best.pt'], batch_size=64, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=True, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=yolov5_train, name=validation_on_test_data, exist_ok=False, half=False, dnn=False\n",
            "YOLOv5 🚀 v7.0-290-gb2ffe055 Python-3.10.12 torch-2.1.0+cu121 CPU\n",
            "\n",
            "Fusing layers... \n",
            "Model summary: 276 layers, 35358588 parameters, 0 gradients, 49.1 GFLOPs\n",
            "\u001b[34m\u001b[1mtest: \u001b[0mScanning /content/test/labels... 1 images, 0 backgrounds, 0 corrupt: 100% 1/1 [00:00<00:00, 30.96it/s]\n",
            "\u001b[34m\u001b[1mtest: \u001b[0mNew cache created: /content/test/labels.cache\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 1/1 [00:01<00:00,  1.17s/it]\n",
            "                   all          1         10      0.956      0.157      0.285      0.124\n",
            "                     0          1          3      0.735      0.941      0.913      0.351\n",
            "                     1          1          2          1          0      0.248      0.128\n",
            "                     3          1          1          1          0      0.124     0.0871\n",
            "                     5          1          1          1          0      0.199     0.0796\n",
            "                     6          1          1          1          0          0          0\n",
            "                     9          1          2          1          0      0.224      0.101\n",
            "Speed: 1.5ms pre-process, 1067.5ms inference, 89.0ms NMS per image at shape (64, 3, 640, 640)\n",
            "Results saved to \u001b[1myolov5_train/validation_on_test_data\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "WEIGHTS_BEST = f\"{PROJECT_NAME}/feature_extraction/weights/best.pt\"\n",
        "! python yolov5/val.py --weights $WEIGHTS_BEST --batch $VAL_BATCH --data 'data.yaml' --task test --project $PROJECT_NAME --name 'validation_on_test_data' --augment"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3b3da04e",
      "metadata": {
        "papermill": {
          "duration": 0.267085,
          "end_time": "2022-06-29T18:46:43.033553",
          "exception": false,
          "start_time": "2022-06-29T18:46:42.766468",
          "status": "completed"
        },
        "tags": [],
        "id": "3b3da04e"
      },
      "source": [
        "# Test detection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "f735f96b",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-29T18:46:43.762361Z",
          "iopub.status.busy": "2022-06-29T18:46:43.761889Z",
          "iopub.status.idle": "2022-06-29T18:46:44.487316Z",
          "shell.execute_reply": "2022-06-29T18:46:44.486241Z"
        },
        "papermill": {
          "duration": 1.188124,
          "end_time": "2022-06-29T18:46:44.489858",
          "exception": false,
          "start_time": "2022-06-29T18:46:43.301734",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f735f96b",
        "outputId": "531afe3b-b997-4d01-c07b-6ef9fd13957d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rm: cannot remove 'yolov5_train/detect_test*': No such file or directory\n"
          ]
        }
      ],
      "source": [
        "# Delete old results\n",
        "wildcard = f\"{PROJECT_NAME}/detect_test*\"\n",
        "! rm -r $wildcard"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "3e231d41",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-29T18:46:45.042626Z",
          "iopub.status.busy": "2022-06-29T18:46:45.042264Z",
          "iopub.status.idle": "2022-06-29T18:46:56.116605Z",
          "shell.execute_reply": "2022-06-29T18:46:56.115487Z"
        },
        "papermill": {
          "duration": 11.349649,
          "end_time": "2022-06-29T18:46:56.118947",
          "exception": false,
          "start_time": "2022-06-29T18:46:44.769298",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3e231d41",
        "outputId": "9a6bd15f-d091-43e1-abdb-dd427c1e0c2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5_train/feature_extraction/weights/best.pt'], source=test/images, data=yolov5/data/coco128.yaml, imgsz=[640, 640], conf_thres=0.6, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_csv=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=True, visualize=False, update=False, project=yolov5_train, name=detect_test, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
            "YOLOv5 🚀 v7.0-290-gb2ffe055 Python-3.10.12 torch-2.1.0+cu121 CPU\n",
            "\n",
            "Fusing layers... \n",
            "Model summary: 276 layers, 35358588 parameters, 0 gradients, 49.1 GFLOPs\n",
            "image 1/1 /content/test/images/b22a9aed-2.jpg: 128x640 (no detections), 1012.9ms\n",
            "Speed: 0.7ms pre-process, 1012.9ms inference, 1.0ms NMS per image at shape (1, 3, 640, 640)\n",
            "Results saved to \u001b[1myolov5_train/detect_test\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "! python yolov5/detect.py --weights $WEIGHTS_BEST --conf 0.6 --source 'test/images' --project $PROJECT_NAME --name 'detect_test' --augment --line=3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "580e01aa",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-29T18:46:56.670847Z",
          "iopub.status.busy": "2022-06-29T18:46:56.670490Z",
          "iopub.status.idle": "2022-06-29T18:46:56.676187Z",
          "shell.execute_reply": "2022-06-29T18:46:56.675065Z"
        },
        "papermill": {
          "duration": 0.281718,
          "end_time": "2022-06-29T18:46:56.678286",
          "exception": false,
          "start_time": "2022-06-29T18:46:56.396568",
          "status": "completed"
        },
        "tags": [],
        "id": "580e01aa"
      },
      "outputs": [],
      "source": [
        "def read_images(dirpath):\n",
        "  images = []\n",
        "  for img_filename in os.listdir(dirpath):\n",
        "    images.append(mpimg.imread(f\"{dirpath}/{img_filename}\"))\n",
        "  return images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "1b35f9a3",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-29T18:46:57.281035Z",
          "iopub.status.busy": "2022-06-29T18:46:57.279956Z",
          "iopub.status.idle": "2022-06-29T18:46:57.291987Z",
          "shell.execute_reply": "2022-06-29T18:46:57.290941Z"
        },
        "papermill": {
          "duration": 0.343036,
          "end_time": "2022-06-29T18:46:57.294323",
          "exception": false,
          "start_time": "2022-06-29T18:46:56.951287",
          "status": "completed"
        },
        "tags": [],
        "id": "1b35f9a3"
      },
      "outputs": [],
      "source": [
        "def label_test_images(test_images_path, test_labels_path, classes):\n",
        "  test_images = os.listdir(test_images_path)\n",
        "  labeled_images = []\n",
        "\n",
        "  for idx, test_image_filename in enumerate(test_images):\n",
        "    image = mpimg.imread(f\"{test_images_path}/{test_image_filename}\")\n",
        "\n",
        "    x_shape, y_shape = image.shape[1], image.shape[0]\n",
        "\n",
        "    test_label_filename = f\"{test_image_filename[:-4]}.txt\"\n",
        "\n",
        "    with open(f\"{test_labels_path}/{test_label_filename}\", \"r\") as f:\n",
        "      lines = f.readlines()\n",
        "\n",
        "      for line in lines:\n",
        "        # Parse line\n",
        "        box = line.split()\n",
        "        class_idx = box[0]\n",
        "\n",
        "        class_name = names[int(class_idx)]\n",
        "        x_center, y_center, box_w, box_h = int(float(box[1])*x_shape), int(float(box[2])*y_shape), int(float(box[3])*x_shape), int(float(box[3])*y_shape)\n",
        "        x1, y1, x2, y2 = x_center-int(box_w/2), y_center-int(box_h/2), x_center+int(box_w/2), y_center+int(box_h/2)\n",
        "\n",
        "        cv2.rectangle(image, (x1, y1), (x2, y2), (0, 0, 255), 3)\n",
        "        cv2.putText(image, class_name, (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 3)\n",
        "\n",
        "    labeled_images.append(image)\n",
        "\n",
        "  return labeled_images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "2338a3db",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-29T18:46:57.841696Z",
          "iopub.status.busy": "2022-06-29T18:46:57.841308Z",
          "iopub.status.idle": "2022-06-29T18:46:58.905221Z",
          "shell.execute_reply": "2022-06-29T18:46:58.904036Z"
        },
        "papermill": {
          "duration": 1.337203,
          "end_time": "2022-06-29T18:46:58.907998",
          "exception": false,
          "start_time": "2022-06-29T18:46:57.570795",
          "status": "completed"
        },
        "tags": [],
        "id": "2338a3db"
      },
      "outputs": [],
      "source": [
        "detect_path = f\"{PROJECT_NAME}/detect_test\"\n",
        "test_images_path = f\"test/images\"\n",
        "test_labels_path = f\"test/labels\"\n",
        "\n",
        "detected_images = read_images(detect_path)\n",
        "test_labeled_images = label_test_images(test_images_path, test_labels_path, classes=names)\n",
        "\n",
        "stacked_images = [np.hstack([detected_images[idx], test_labeled_images[idx]]) for idx in range(len(detected_images))]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "f661fe3d",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-06-29T18:46:59.476771Z",
          "iopub.status.busy": "2022-06-29T18:46:59.476334Z",
          "iopub.status.idle": "2022-06-29T18:47:10.876031Z",
          "shell.execute_reply": "2022-06-29T18:47:10.875042Z"
        },
        "papermill": {
          "duration": 11.682689,
          "end_time": "2022-06-29T18:47:10.880221",
          "exception": false,
          "start_time": "2022-06-29T18:46:59.197532",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "f661fe3d",
        "outputId": "0f14a5f0-6922-42bd-cfae-fbfa4ae5f2b5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 4000x1500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for image in stacked_images:\n",
        "  fig = plt.figure(figsize=(40, 15))\n",
        "  ax1 = fig.add_subplot(2,2,1)\n",
        "  ax1.imshow(image)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d2b87a72",
      "metadata": {
        "papermill": {
          "duration": 0.358482,
          "end_time": "2022-06-29T18:47:11.591433",
          "exception": false,
          "start_time": "2022-06-29T18:47:11.232951",
          "status": "completed"
        },
        "tags": [],
        "id": "d2b87a72"
      },
      "source": [
        "# Save model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "90d46df8",
      "metadata": {
        "papermill": {
          "duration": 0.353186,
          "end_time": "2022-06-29T18:47:12.341083",
          "exception": false,
          "start_time": "2022-06-29T18:47:11.987897",
          "status": "completed"
        },
        "tags": [],
        "id": "90d46df8"
      },
      "source": [
        "To save your model just download best.pt file from PROJECT_FOLDER -> feature_extraction (your best) -> weights -> best.pt\n",
        "\n",
        "File best.pt will be used to load it in your project to predict."
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.4"
    },
    "papermill": {
      "default_parameters": {},
      "duration": 3131.508917,
      "end_time": "2022-06-29T18:47:13.519249",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2022-06-29T17:55:02.010332",
      "version": "2.3.4"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}